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Identity matching using personal and social identity features

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Abstract

Identity verification is essential in our mission to identify potential terrorists and criminals. It is not a trivial task because terrorists reportedly assume multiple identities using either fraudulent or legitimate means. A national identification card and biometrics technologies have been proposed as solutions to the identity problem. However, several studies show their inability to tackle the complex problem. We aim to develop data mining alternatives that can match identities referring to the same individual. Existing identity matching techniques based on data mining primarily rely on personal identity features. In this research, we propose a new identity matching technique that considers both personal identity features and social identity features. We define two groups of social identity features including social activities and social relations. The proposed technique is built upon a probabilistic relational model that utilizes a relational database structure to extract social identity features. Experiments show that the social activity features significantly improve the matching performance while the social relation features effectively reduce false positive and false negative decisions.

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Authors and Affiliations

  1. College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA

    Jiexun Li

  2. Pamplin College of Business, Virginia Tech, Blacksburg, VA, 24060, USA

    G. Alan Wang

  3. Department of MIS, Eller College of Management, University of Arizona, 1130 E Helen Street Room 430, Tucson, AZ, 85721, USA

    Hsinchun Chen

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  1. Jiexun Li
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  2. G. Alan Wang
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  3. Hsinchun Chen
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Correspondence to Jiexun Li.

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Li, J., Wang, G.A. & Chen, H. Identity matching using personal and social identity features. Inf Syst Front 13, 101–113 (2011). https://doi.org/10.1007/s10796-010-9270-0

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